"""
=======================================
Metrics specific to imbalanced learning
=======================================
Specific metrics have been developed to evaluate classifier which
has been trained using imbalanced data. :mod:`imblearn` provides mainly
two additional metrics which are not implemented in :mod:`sklearn`: (i)
geometric mean and (ii) index balanced accuracy.
"""
# Authors: Guillaume Lemaitre
# License: MIT
# %%
print(__doc__)
RANDOM_STATE = 42
# %% [markdown]
# First, we will generate some imbalanced dataset.
# %%
from sklearn.datasets import make_classification
X, y = make_classification(
n_classes=3,
class_sep=2,
weights=[0.1, 0.9],
n_informative=10,
n_redundant=1,
flip_y=0,
n_features=20,
n_clusters_per_class=4,
n_samples=5000,
random_state=RANDOM_STATE,
)
# %% [markdown]
# We will split the data into a training and testing set.
# %%
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
X, y, stratify=y, random_state=RANDOM_STATE
)
# %% [markdown]
# We will create a pipeline made of a :class:`~imblearn.over_sampling.SMOTE`
# over-sampler followed by a :class:`~sklearn.linear_model.LogisticRegression`
# classifier.
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from imblearn.over_sampling import SMOTE
# %%
from imblearn.pipeline import make_pipeline
model = make_pipeline(
StandardScaler(),
SMOTE(random_state=RANDOM_STATE),
LogisticRegression(max_iter=10_000, random_state=RANDOM_STATE),
)
# %% [markdown]
# Now, we will train the model on the training set and get the prediction
# associated with the testing set. Be aware that the resampling will happen
# only when calling `fit`: the number of samples in `y_pred` is the same than
# in `y_test`.
# %%
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
# %% [markdown]
# The geometric mean corresponds to the square root of the product of the
# sensitivity and specificity. Combining the two metrics should account for
# the balancing of the dataset.
# %%
from imblearn.metrics import geometric_mean_score
print(f"The geometric mean is {geometric_mean_score(y_test, y_pred):.3f}")
# %% [markdown]
# The index balanced accuracy can transform any metric to be used in
# imbalanced learning problems.
# %%
from imblearn.metrics import make_index_balanced_accuracy
alpha = 0.1
geo_mean = make_index_balanced_accuracy(alpha=alpha, squared=True)(geometric_mean_score)
print(
f"The IBA using alpha={alpha} and the geometric mean: "
f"{geo_mean(y_test, y_pred):.3f}"
)
# %%
alpha = 0.5
geo_mean = make_index_balanced_accuracy(alpha=alpha, squared=True)(geometric_mean_score)
print(
f"The IBA using alpha={alpha} and the geometric mean: "
f"{geo_mean(y_test, y_pred):.3f}"
)